Analysis of Benchmark Forecasting Models Versus a Recurrent Neural Network Forecast of Channel Cargo Demand
Abstract
Given the longstanding history of inaccurate cargo demand forecast and underutilization of contracted commercial airlift augmentation, the primary objective of this paper is to present a simple forecasting model developed using Python. This study compares benchmark time series forecasting models against a Recurrent Neural Network model to determine which model is best for predicting monthly channel cargo airlift demand. To determine which model produces the best forecast, univariate time series analysis is conducted on Integrated Development Environment/Global Transportation Network Convergence data using readily available statistical modules and Machine Learning algorithms within the Python ecosystem.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 03, 2022
- Accession Number
- AD1177711
Entities
People
- Abraham N Umanah
Organizations
- Air Force Institute of Technology